Summary

The Department: Platform 

The Data Platform Engineering team plays a crucial role in building, scaling, and maintaining the data infrastructure, with a focus on reliability, availability, and performance. Working closely with Engineering and Platform, Data Platform Engineering ensures a robust foundation for developing and scaling data-driven products and solutions. This includes implementing advanced scaling strategies for database systems and maintaining core infrastructure components such as streaming, and real-time/batch processing tools. These efforts support a resilient, high-performing platform that enables the organization to react to changing conditions with minimal downtime and maximum efficiency.

The Role: Staff Data/Database Platform Engineer

As a Staff Data Platform Engineer, you're a part of the Data/Database Platform Engineering Team and you’ll be instrumental in leading building, scaling, and maintaining our data infrastructure with a focus on architecture, reliability, availability, and performance. This role will work closely with both data engineering and product engineering teams, providing a robust infrastructure foundation that enables them to build, maintain, and scale data-driven products and solutions. An immediate priority will be implementing advanced scaling strategies for our relational database systems to support a highly scalable infrastructure. 

This role also requires a strong commitment to uptime and incident response, including participation in an on-call rotation. You’ll bring expertise in database technologies (relational, columnar, document, key-value, and unstructured) and familiarity with core data infrastructure components like message queues, ETL pipelines, and real-time processing tools to support a resilient, high-performing data platform.

Responsibilities:

1. Database Scaling and Optimization: Design and implement scaling strategies for relational systems to ensure they meet the high availability and scalability needs of data and product engineering teams.
2. Availability and Uptime Management: Proactively monitor and optimize database systems to meet stringent uptime requirements. Participate in an on-call rotation to respond to incidents, troubleshoot issues, and restore service promptly during disruptions.
3. Architect and Optimize Database Infrastructure: Manage a variety of database technologies, balancing tradeoffs across relational, columnar, document, key-value, and unstructured data solutions, providing a foundation for data warehousing and supporting data-driven product needs.
4. Integration with Data Engineering and Product Pipelines: Collaborate with data and product engineering teams to implement and optimize data pipelines, including message queues (e.g., Kafka), ETL workflows, and real-time processing, ensuring efficient and reliable data movement.
5. Infrastructure Automation and Reliability: Utilize infrastructure as code (IaC) to automate deployment, scaling, and maintenance, creating a consistent, reliable environment that supports high availability and deployment efficiency for both data and product teams.
6. Performance Tuning and Incident Response: Conduct performance tuning, establish monitoring and alerting, and address potential issues quickly to ensure a responsive platform that meets the needs of all engineering workloads.
7. Documentation and Knowledge Sharing: Document processes, including scaling strategies, monitoring setups, and best practices, to support alignment with engineering requirements and ensure smooth handoffs in on-call situations.

Qualifications:

1. Deep expertise in data and storage technologies, including RDBMS (e.g., Postgres), NoSQL, and other database types (e.g., columnar, document, key-value, and unstructured), with a strong understanding of tradeoffs and use cases for each.
2. Demonstrated experience with advanced database scaling strategies for relational systems.
3. Strong knowledge of high-availability architectures and proficiency with monitoring tools to support uptime and incident response.
4. Experience with cloud-based database and data processing platforms, such as Amazon Aurora, Databricks, AWS RDS, Redshift, BigQuery, Snowflake, and managed services like AWS EMR and Google Cloud Dataflow.
5. Familiarity with message queues, ETL workflows, and data pipelines for real-time and batch processing.
6. Strong programming skills (e.g., Python, Bash, SQL) and experience with CI/CD practices.
7. Experience in an on-call rotation and handling incident response.
8. Excellent communication and collaboration skills, with a proven ability to work effectively with data and product engineering teams.

Salary Range: The base salary range for this role is between $172,000 - $241,000 in the State of New York, the State of California and the State of Washington. This range is not inclusive of our discretionary bonus or equity package. When determining a candidate’s compensation, we consider a number of factors including skillset, experience, job scope, and current market data.

Skills

• Communications Skills
• Database Management
• Development
• Python
• Software Architecture
• Software Engineering
• SQL
• Strategic Thinking
• Team Collaboration